Results for “correlation model”

NASA NTRS2017

Using Dispersed Modes During Model Correlation

The model correlation process for the modal characteristics of a launch vehicle is well established. After a test, parameters within the nominal model are adjusted to reflect structural dynamics revealed during testing. However, a full model correlation process for a complex structure can take months of man-hours and many computational resources. If the analyst only has weeks, or even days, of time in which to correlate the nominal model to the experimental results, then the traditional correlation process is not suitable. This paper describes using model dispersions to assist the model correlation process and decrease the overall cost of the process. The process creates thousands of model dispersions from the nominal model prior to the test and then compares each of them to the test data. Using mode shape and frequency error metrics, one dispersion is selected as the best match to the test data. This dispersion is further improved by using a commercial model correlation software. In the three examples shown in this paper, this dispersion based model correlation process performs well when compared to models correlated using traditional techniques and saves time in the post-test analysis.

Stewart, Eric C.↗

NASA NTRSDate not supplied

Model Correlation and Thermal Analysis of xEMU Boot at Lunar South Pole Temperatures

The National Aeronautics and Space Administration (NASA) Artemis program plans to send astronauts to the lunar south pole, a region of the moon that is colder than previous lunar missions and low earth orbit operations. The spacesuit boots that will be used on these missions will be directly impacted by these extremely cold temperatures (down to ~50 K). To assess the performance of the Exploration Extravehicular Mobility Unit (xEMU) lunar boot in these extreme temperatures, the boot was tested at the Jet Propulsion Lab (JPL) in the Cryogenic Ice Transfer, Acquisition Development, and Excavation Laboratory (CITADEL) thermal vacuum (TVAC) chamber. This data was used to correlate thermal models to predict operational performance of the boots on the lunar south pole. This paper documents the data analysis, thermal boot model correlation, and operational predictions conducted using data from the xEMU TVAC test. Data from the test series was used to determine expected thermal conductances within the boot and between the boot and environment. These conductances were used to correlate a Thermal Desktop model of the xEMU boot across 10 different test points which varied external temperature, internal boot ventilation flowrate, and contact pressure. The correlated model was then used to predict operational performance in the lunar south pole. While the predictions indicate promising evidence for performance of the boots at the 100K test point, there is still substantial uncertainty in performance, particularly at the 48K test point. The results of this test series and model correlation stress the importance of improved testing for characterizing the expected thermal resistance between the outside of the boot and the lunar surface.

thermal analysis↗

NASA NTRS2026

Model Correlation and Thermal Analysis of xEMU Boot at Lunar South Pole Temperatures

The National Aeronautics and Space Administration (NASA) Artemis program plans to send astronauts to the lunar south pole, a region of the moon that is colder than previous lunar missions and low earth orbit operations. The spacesuit boots that will be used on these missions will be directly impacted by these extremely cold temperatures (down to ~50 K). To assess the performance of the Exploration Extravehicular Mobility Unit (xEMU) lunar boot in these extreme temperatures, the boot was tested at the Jet Propulsion Lab (JPL) in the Cryogenic Ice Transfer, Acquisition Development, and Excavation Laboratory (CITADEL) thermal vacuum (TVAC) chamber. This data was used to correlate thermal models to predict operational performance of the boots on the lunar south pole. This paper documents the data analysis, thermal boot model correlation, and operational predictions conducted using data from the xEMU TVAC test. Data from the test series was used to determine expected thermal conductances within the boot and between the boot and environment. These conductances were used to correlate a Thermal Desktop model of the xEMU boot across 10 different test points which varied external temperature, internal boot ventilation flowrate, and contact pressure. The correlated model was then used to predict operational performance in the lunar south pole. While the predictions indicate promising evidence for performance of the boots at the 100K test point, there is still substantial uncertainty in performance, particularly at the 48K test point. The results of this test series and model correlation stress the importance of improved testing for characterizing the expected thermal resistance between the outside of the boot and the lunar surface.

xEMU↗

NASA NTRSDate not supplied

L'Ralph's Advanced Thermal Model Correlation Using Veritrek

Thermal model correlation uses data from thermal balance tests to better estimate uncertain input parameter values. During the correlation process, input parameters are modified in an iterative manner which can become computationally expensive since this requires that the high-fidelity thermal model be run for each iteration. Depending on the number of thermal balance test points there can be many sets of correlation parameters that satisfy correlation criteria; and having enough data to ascertain the best set of correlation parameters to use, further increases the computational expense. Reduced-order models (ROMs) provide computationally efficient surrogates of high-fidelity models and are often built to reduce development cycle times and cost. By leveraging the speed of reduced-order models and the Correlation Analysis feature in the Veritrek software, the typical computational expense of a traditional thermal model correlation process can be significantly reduced and having access to hundreds of thousands of iteration results provides an advanced means of intelligently determining the best set of correlation parameters to use. The L’Ralph thermal team at NASA Goddard Space Flight Center explored the use of the Veritrek software for their thermal model correlation efforts. The ROM that was created allowed for the variation of 15 input parameters to match 70 temperature sensor readouts from 3 thermal balance plateus and required 125 runs of the high-fidelity Thermal Desktop® model to generate a ROM that could predict the detailed model’s results to within 0.2 K (RMS). The ROM was then used to find dozens of plausible correlation parameter values based on L’Ralph instrument test data within a few seconds. By providing several plausible correlation parameter combinations, Veritrek allowed the thermal team to explore different uncertain parameter value combinations and provided insight into how deterministic each input parameter was. This allowed for a more confident decision on the best set of correlation parameters to use, compared to traditional model correlation techniques. In this presentation, the L’Ralph thermal team will be presenting their experience with the Veritrek software and how the software was utilized to provide additional insights during the correlation process.

Daniel Bae↗

NASA NTRSDate not supplied

L'Ralph's Advanced Thermal Model Correlation Using Veritrek

Thermal model correlation uses data from thermal balance tests to better estimate uncertain input parameter values. During the correlation process, input parameters are modified in an iterative manner which can become computationally expensive since this requires that the high-fidelity thermal model be run for each iteration. Depending on the number of thermal balance test points there can be many sets of correlation parameters that satisfy correlation criteria; and having enough data to ascertain the best set of correlation parameters to use, further increases the computational expense. Reduced-order models (ROMs) provide computationally efficient surrogates of high-fidelity models and are often built to reduce development cycle times and cost. By leveraging the speed of reduced-order models and the Correlation Analysis feature in the Veritrek software, the typical computational expense of a traditional thermal model correlation process can be significantly reduced and having access to hundreds of thousands of iteration results provides an advanced means of intelligently determining the best set of correlation parameters to use. The L’Ralph thermal team at NASA Goddard Space Flight Center explored the use of the Veritrek software for their thermal model correlation efforts. The ROM that was created allowed for the variation of 15 input parameters to match 70 temperature sensor readouts from 3 thermal balance plateus and required 125 runs of the high-fidelity Thermal Desktop® model to generate a ROM that could predict the detailed model’s results to within 0.2 K (RMS). The ROM was then used to find dozens of plausible correlation parameter values based on L’Ralph instrument test data within a few seconds. By providing several plausible correlation parameter combinations, Veritrek allowed the thermal team to explore different uncertain parameter value combinations and provided insight into how deterministic each input parameter was. This allowed for a more confident decision on the best set of correlation parameters to use, compared to traditional model correlation techniques. In this presentation, the L’Ralph thermal team will be presenting their experience with the Veritrek software and how the software was utilized to provide additional insights during the correlation process. "

Daniel Bae↗

NASA NTRS2017

Using Dispersed Modes During Model Correlation

Using model dispersions as a starting point allows us to quickly adjust a model to reflect new test data: a) The analyst does a lot of work before the test to save time post-test. b) Creating 1000s of model dispersions to provide "coarse tuning," then use Attune to provide the "fine tuning." Successful model tuning on three structures: a) TAURUS. b) Ares I-X C) Cart (in backup charts). Mode weighting factors, matrix norm method, and XOR vs. MAC all play key roles in determining the BME. The BME process will be used on future tests: a) ISPE modal test (ongoing work). b) SLS modal test (mid 2018).

Stewart, Eric↗

NASA NTRS2017

Mars 2020 Mobility Actuator Thermal Testing and Model Correlation

This paper describes the thermal testing and model correlation of a mobility actuator planned for use on the Mars 2020 Rover. The mobility actuator is identical to those which have been successfully flown and operated on the Mars Science Laboratory (MSL) Curiosity rover since its successful landing on Mars in August of 2012. The actuator consists of a motor, brake, and encoder paired with a four stage planetary gear box. In this thermal test, the actuator was instrumented with a number of thermocouples on both the interior and exterior of the gearbox. Heaters and a cold plate were used to generate thermal gradients across the actuator in vacuum, low pressure GN2, and low pressure CO2 environments in an effort to correlate a thermal model and develop a better understanding of how heat flows through the mechanism. This testing resulted in the successful model correlation of a simplified thermal model, and yielded important insights regarding the conductance of ball bearings and gear-to-gear contact. Ball bearing thermal conductance in a low pressure environment can be estimated by using correlations for vacuum thermal conductance along with a multiplier to account for increased gas conduction, and gear-to-gear conductance can be estimated by accounting for gas and grease conduction between two gears.

Novak, Keith↗

NASA NTRS1991

Model correlation and damage location for large space truss structures: Secant method development and evaluation

On-orbit testing of a large space structure will be required to complete the certification of any mathematical model for the structure dynamic response. The process of establishing a mathematical model that matches measured structure response is referred to as model correlation. Most model correlation approaches have an identification technique to determine structural characteristics from the measurements of the structure response. This problem is approached with one particular class of identification techniques - matrix adjustment methods - which use measured data to produce an optimal update of the structure property matrix, often the stiffness matrix. New methods were developed for identification to handle problems of the size and complexity expected for large space structures. Further development and refinement of these secant-method identification algorithms were undertaken. Also, evaluation of these techniques is an approach for model correlation and damage location was initiated.

Smith, Suzanne Weaver↗

NASA NTRS2018

Comparing Free-Free and Shaker Table Model Correlation Methods Using Jim Beam

Finite element model correlation as part of a spacecraft program has always been a challenge. For any NASA mission, the coupled system response of the spacecraft and launch vehicle can be determined analytically through a Coupled Loads Analysis (CLA), as it is not possible to test the spacecraft and launch vehicle coupled system before launch. The value of the CLA is highly dependent on the accuracy of the frequencies and mode shapes extracted from the spacecraft model. NASA standards require the spacecraft model used in the final Verification Loads Cycle to be correlated by either a modal test or by comparison of the model with Frequency Response Functions (FRFs) obtained during the environmental qualification test. Due to budgetary and time constraints, most programs opt to correlate the spacecraft dynamic model during the environmental qualification test, conducted on a large shaker table. For any model correlation effort, the key has always been finding a proper definition of the boundary conditions. This paper is a correlation case study to investigate the difference in responses of a simple structure using a free-free boundary, a fixed boundary on the shaker table, and a base-drive vibration test, all using identical instrumentation. The NAVCON Jim Beam test structure, featured in the IMAC round robin modal test of 2009, was selected as a simple, well recognized and well characterized structure to conduct this investigation. First, a free-free impact modal test of the Jim Beam was done as an experimental control. Second, the Jim Beam was mounted to a large 20,000 lbf shaker, and an impact modal test in this fixed configuration was conducted. Lastly, a vibration test of the Jim Beam was conducted on the shaker table. The free-free impact test, the fixed impact test, and the base-drive test were used to assess the effect of the shaker modes, evaluate the validity of fixed-base modeling assumptions, and compare final model correlation results between these boundary conditions.

Modal Test↗

NASA NTRSDate not supplied

How to Educate Decision Makers on the Value and Necessity of Modal Testing and Model Correlation: Tips for Young Engineers

Engineers need to effectively communicate the justification and value of their modal testing and model correlation in terminology familiar to decision makers as it relates to the program’s risk tolerance. This communication must relate to the program’s risk tolerance and the metrics used to judge the performance of both the program and individual decision makers. The challenge is the terminologies familiar to engineers and decision makers are quite different and seemingly unrelated. The engineering profession has developed a specific terminology to solve highly technical issues, which are many times themselves unique to very specific engineering problems. It is all too easy for engineers to believe that everyone in their organization, including the decision makers, has an intrinsic understanding of what they do and the value it brings to the program’s success. This is especially true for young engineers who have recently spent the last four plus years in an academic engineering learning environment, which has a highly technical research oriented atmosphere. Effective communication with decision makers is increasingly important as the technical breadth and practical program and project experience level for up and coming decision makers diminishes. It is not unusual for the decision makers to have technical knowledge in a domain different from structural dynamics (e.g., electronics or systems). Competition among satellite manufactures has increased the focus on programmatic cost and ability to deliver on schedule. NASA programs are also seeing more restrictive programmatic cost and schedule constraints, which impact both analysis and testing. It should also be noted that a comprehensive suite of tests are required to verify a satellite’s design capability with some margin. These tests include static strength verification tests, shock, acoustic, and vibration tests (sine and random) of systems, subsystems, and components. Each of these verification tests provide opportunities for model correlation and risk reduction. It is important to recognize dynamic loads/modal test models may not include all of the flight hardware (i.e., harness, coax, waveguides, connectors, etc.) and the previously mentioned tests are still required for qualification/verification of the design. This paper provides tips to young engineers on how to bridge this communications gap, have a better understanding of the environment in which decision makers operate, and assist them to better support successful missions. While this paper primarily focuses on modal testing and model correlation as related to spacecraft missions, the concepts and recommendations presented here are equally applicable to other fields such as aeronautics, automotive, power generation, etc.

Decision Maker↗

NASA NTRSDate not supplied

How to Educate Decision Makers on the Value and Necessity of Modal Testing and Model Correlation: Tips for Young Engineers

Engineers need to effectively communicate the justification and value of their modal testing and model correlation in terminology familiar to decision makers as it relates to the program’s risk tolerance. This communication must relate to the program’s risk tolerance and the metrics used to judge the performance of both the program and individual decision makers. The challenge is the terminologies familiar to engineers and decision makers are quite different and seemingly unrelated. The engineering profession has developed a specific terminology to solve highly technical issues, which are many times themselves unique to very specific engineering problems. It is all too easy for engineers to believe that everyone in their organization, including the decision makers, has an intrinsic understanding of what they do and the value it brings to the program’s success. This is especially true for young engineers who have recently spent the last four plus years in an academic engineering learning environment, which has a highly technical research oriented atmosphere. Effective communication with decision makers is increasingly important as the technical breadth and practical program and project experience level for up and coming decision makers diminishes. It is not unusual for the decision makers to have technical knowledge in a domain different from structural dynamics (e.g., electronics or systems). Competition among satellite manufactures has increased the focus on programmatic cost and ability to deliver on schedule. NASA programs are also seeing more restrictive programmatic cost and schedule constraints, which impact both analysis and testing. It should also be noted that a comprehensive suite of tests are required to verify a satellite’s design capability with some margin. These tests include static strength verification tests, shock, acoustic, and vibration tests (sine and random) of systems, subsystems, and components. Each of these verification tests provide opportunities for model correlation and risk reduction. It is important to recognize dynamic loads/modal test models may not include all of the flight hardware (i.e., harness, coax, waveguides, connectors, etc.) and the previously mentioned tests are still required for qualification/verification of the design. This paper provides tips to young engineers on how to bridge this communications gap, have a better understanding of the environment in which decision makers operate, and assist them to better support successful missions. While this paper primarily focuses on modal testing and model correlation as related to spacecraft missions, the concepts and recommendations presented here are equally applicable to other fields such as aeronautics, automotive, power generation, etc.

Decision Maker↗

NASA NTRS2015

Thermal Testing and Model Correlation of the Magnetospheric Multiscale (MMS) Observatories

The Magnetospheric Multiscale (MMS) mission is a Solar Terrestrial Probes mission comprising four identically instrumented spacecraft that will use Earth's magnetosphere as a laboratory to study the microphysics of three fundamental plasma processes: magnetic reconnection, energetic particle acceleration, and turbulence. This paper presents the complete thermal balance (TB) test performed on the first of four observatories to go through thermal vacuum (TV) and the minibalance testing that was performed on the subsequent observatories to provide a comparison of all four. The TV and TB tests were conducted in a thermal vacuum chamber at the Naval Research Laboratory (NRL) in Washington, D.C. with the vacuum level higher than 1.3 x 10 (sup -4) pascals (10 (sup -6) torr) and the surrounding temperature achieving -180 degrees Centigrade. Three TB test cases were performed that included hot operational science, cold operational science and a cold survival case. In addition to the three balance cases a two hour eclipse and a four hour eclipse simulation was performed during the TV test to provide additional transient data points that represent the orbit in eclipse (or Earth's shadow) The goal was to perform testing such that the flight orbital environments could be simulated as closely as possible. A thermal model correlation between the thermal analysis and the test results was completed. Over 400 1-Wire temperature sensors, 200 thermocouples and 125 flight thermistor temperature sensors recorded data during TV and TB testing. These temperature versus time profiles and their agreements with the analytical results obtained using Thermal Desktop and SINDA/FLUINT are discussed. The model correlation for the thermal mathematical model (TMM) is conducted based on the numerical analysis results and the test data. The philosophy of model correlation was to correlate the model to within 3 degrees Centigrade of the test data using the standard deviation and mean deviation error calculation. Individual temperature error goal is to be within 5 degrees Centigrade and the heater power goal is to be within 5 percent of test data. The results of the model correlation are discussed and the effect of some material and interface parameters on the temperature profiles are presented.

Correlation↗

NASA NTRS2015

Thermal Testing and Model Correlation of the Magnetospheric Multiscale (MMS) Observatories

International Conference on Envronmental Systems (ICES), Seattle WA NCTS 20964-15. The Magnetospheric Multiscale (MMS) mission is a Solar Terrestrial Probes mission comprising four identically instrumented spacecraft that will use Earths magnetosphere as a laboratory tostudy the microphysics of three fundamental plasma processes: magnetic reconnection, energetic particle acceleration, and turbulence. This paper presents the complete thermal balance (TB) test performed on the first of four observatories to go through thermal vacuum (TV) and the minibalance testing that was performed on the subsequent observatories to provide a comparison of all four. The TV and TB tests were conducted in a thermal vacuum chamber at the Naval Research Laboratory (NRL) in Washington, D.C. with the vacuum level higher than 1.3 x 10-4 Pa (10-6 torr)and the surrounding temperature achieving -180 C. Three TB test cases were performed that included hot operational science, cold operational science and a cold survival case. In addition to the three balance cases a two hour eclipse and a four hour eclipse simulation was performed during the TV test to provide additional transient data points that represent the orbit in eclipse (or Earth's shadow) The goal was to perform testing such that the flight orbital environments could be simulated as closely as possible. A thermal model correlation between the thermal analysis and the test results was completed. Over 400 1-Wire temperature sensors, 200 thermocouples and 125 flight thermistor temperature sensors recorded data during TV and TB testing. These temperatureversus time profiles and their agreements with the analytical results obtained using Thermal Desktop and SINDAFLUINT are discussed. The model correlation for the thermal mathematical model (TMM) is conducted based on the numerical analysis results and the test data. The philosophy of model correlation was to correlate the model to within 3 C of the test data using the standard deviation and mean deviation error calculation. Individual temperature error goal is to be within 5 C and the heater power goal is to be within 5 of test data. The results of the model correlation are discussed and the effect of some material and interface parameters on the temperature profiles are presented.

Thermal↗

NASA NTRS2005

Fluids and Combustion Facility: Fluids Integrated Rack Modal Model Correlation

The Fluids Integrated Rack (FIR) is one of two racks in the Fluids and Combustion Facility on the International Space Station. The FIR is dedicated to the scientific investigation of space system fluids management supporting NASA s Exploration of Space Initiative. The FIR hardware was modal tested and FIR finite element model updated to satisfy the International Space Station model correlation criteria. The final cross-orthogonality results between the correlated model and test mode shapes was greater than 90 percent for all primary target modes.

McNelis, Mark E.↗

NASA NTRS2011

FASTSAT-HSV01 Thermal Math Model Correlation

This paper summarizes the thermal math model correlation effort for the Fast Affordable Science and Technology SATellite (FASTSAT-HSV01), which was designed, built and tested by NASA's Marshall Space Flight Center (MSFC) and multiple partners. The satellite launched in November 2010 on a Minotaur IV rocket from the Kodiak Launch Complex in Kodiak, Alaska. It carried three Earth science experiments and two technology demonstrations into a low Earth circular orbit with an inclination of 72deg and an altitude of 650 kilometers. The mission has been successful to date with science experiment activities still taking place daily. The thermal control system on this spacecraft was a passive design relying on thermo-optical properties and six heaters placed on specific components. Flight temperature data is being recorded every minute from the 48 Resistance Temperature Devices (RTDs) onboard the satellite structure and many of its avionics boxes. An effort has been made to correlate the thermal math model to the flight temperature data using Cullimore and Ring's Thermal Desktop and by obtaining Earth and Sun vector data from the Attitude Control System (ACS) team to create an "as-flown" orbit. Several model parameters were studied during this task to understand the spacecraft's sensitivity to these changes. Many "lessons learned" have been noted from this activity that will be directly applicable to future small satellite programs.

McKelvey, Callie↗

NASA NTRS1999

On-Orbit Thermal Performance and Model Correlation of the Fast Auroral Snapshot Explorer

The Fast Auroral SnapshoT explorer (FAST) spacecraft, the second of NASA's Small Explorer (SMEX) series of scientific satellites, was launched on August 21, 1996 by a Pegasus XL launch vehicle. Due to slightly higher than expected temperatures during early orbit operations, an extensive thermal model correlation effort was undertaken to understand and characterize FAST's thermal performance in order to properly orient the spacecraft's attitude during its mission. FAST's thermal design and the on-orbit thermal model correlation and resolution are described. Finally, the correlated model's predictions are compared with nine months of flight data.

Parrish, Keith↗

NASA NTRS1970

Subsystems test bed thermal mathematical model correlation

Subsystems test bed thermal mathematical model correlation - graphs

Mobley, T. B.↗

NASA NTRS2016

Thermal Testing and Model Correlation for Advanced Topographic Laser Altimeter Instrument (ATLAS)

The Advanced Topographic Laser Altimeter System (ATLAS) part of the Ice Cloud and Land Elevation Satellite 2 (ICESat-2) is an upcoming Earth Science mission focusing on the effects of climate change. The flight instrument passed all environmental testing at GSFC (Goddard Space Flight Center) and is now ready to be shipped to the spacecraft vendor for integration and testing. This topic covers the analysis leading up to the test setup for ATLAS thermal testing as well as model correlation to flight predictions. Test setup analysis section will include areas where ATLAS could not meet flight like conditions and what were the limitations. Model correlation section will walk through changes that had to be made to the thermal model in order to match test results. The correlated model will then be integrated with spacecraft model for on-orbit predictions.

Thermal↗